Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection

TL;DR

This paper introduces a novel depth-driven geometric prompt learning network, D2GPLand, for detecting liver landmarks in laparoscopic surgery, leveraging a new dataset and achieving state-of-the-art accuracy to assist surgeons.

Contribution

The paper presents a new dataset L3D, a depth-aware prompt embedding module, and a geometric augmentation scheme, advancing landmark detection in laparoscopic liver surgery.

Findings

D2GPLand achieves 63.52% DICE score.

The method outperforms 12 existing detection approaches.

Provides intuitive guidance for surgeons in laparoscopic scenarios.

Abstract

Laparoscopic liver surgery poses a complex intraoperative dynamic environment for surgeons, where remains a significant challenge to distinguish critical or even hidden structures inside the liver. Liver anatomical landmarks, e.g., ridge and ligament, serve as important markers for 2D-3D alignment, which can significantly enhance the spatial perception of surgeons for precise surgery. To facilitate the detection of laparoscopic liver landmarks, we collect a novel dataset called L3D, which comprises 1,152 frames with elaborated landmark annotations from surgical videos of 39 patients across two medical sites. For benchmarking purposes, 12 mainstream detection methods are selected and comprehensively evaluated on L3D. Further, we propose a depth-driven geometric prompt learning network, namely D2GPLand. Specifically, we design a Depth-aware Prompt Embedding (DPE) module that is guided by self-supervised prompts and generates semantically relevant geometric information with the benefit of global depth cues extracted from SAM-based features. Additionally, a Semantic-specific Geometric Augmentation (SGA) scheme is introduced to efficiently merge RGB-D spatial and geometric information through reverse anatomic perception. The experimental results indicate that D2GPLand obtains state-of-the-art performance on L3D, with 63.52% DICE and 48.68% IoU scores. Together with 2D-3D fusion technology, our method can directly provide the surgeon with intuitive guidance information in laparoscopic scenarios.